8936 results about "Computation complexity" patented technology

A scheme for stereo and multi-channel synthesis of inter-channel correlation (ICC) (normalized cross-correlation) cues for parametric stereo and multi-channel coding. The scheme synthesizes ICC cues such that they approximate those of the original. For that purpose, diffuse audio channels are generated and mixed with the transmitted combined (e.g., sum) signal(s). The diffuse audio channels are preferably generated using relatively long filters with exponentially decaying Gaussian impulse responses. Such impulse responses generate diffuse sound similar to late reverberation. An alternative implementation for reduced computational complexity is proposed, where inter-channel level difference (ICLD), inter-channel time difference (ICTD), and ICC synthesis are all carried out in the domain of a single short-time Fourier transform (STFT), including the filtering for diffuse sound generation.

Methods are provided for embedding auxiliary information in a host content signal which reduce the memory, bandwidth and computational complexity of the embedding and transmission systems. In one embodiment, a first reduced-scale signal is produced which corresponds to the host content embedded with a first logical value and producing a second reduced-scale signal corresponding to the host content embedded with a second logical value. A first set of segments from the first reduced-scale signal may be combined with a second set of segments from the second reduced-scale signal in a pre-defined manner to produce a composite embedded host content. Thus the storage and transmission requirements of the watermarking system are reduced to having to deal with only the original content plus two reduced-scale signals.

Pattern recognition, wherein a sequence of feature vectors is formed from a digitized incoming signal, the feature vectors comprising feature vector components, and at least one feature vector is compared with templates of candidate patterns by computing a distortion measure. A control signal based on at least one time-dependent variable of the recognition process is formulated, and the distortion measure is computed using only a subset of the vector components of the feature vector, the subset being chosen in accordance with said control signal. This reduces the computational complexity of the computation, as the dimensionality of the vectors involved in the computation is effectively reduced. Although such a dimension reduction decreases the computational need, it has been found not to significantly impair the classification performance.

The invention relates to a device and a method for identifying and evaluating an emergency hot topic. The device is provided with four component parts including a text acquisition unit, a text expression unit, a topic clustering unit and a topic evaluation unit. The device and the method are characterized in that only a title, introduction, relevant features and other information of a text of a news report are extracted and regarded as an effective sample set identified by the hot topic. Compared with the existing full text extraction, the experiment result shows that the result of the existing full text extraction is similar to the result of the partial text extraction, but the operation of the device and the method is greatly simplified. Compared with a classical model, an improved feature weight calculation model is good in execution efficiency and strong in adaptability of text representation capability. The model is used for evaluating the hot value of clustered topics, and the calculated hot topic accords with the expected effect and is adaptive to the features of the emergency news report. In a word, in the process of processing the text of the emergency news report, the device and the method have good performance in aspects of calculation complexity, result accuracy and timeliness.

A system and method for on-the-fly detection of scene changes within a video stream through statistical analysis of a portion of the macroblocks comprising each video frame as they are processed using inter-frame coding. If the statistical analysis of the selected macroblocks of the current frame differs from the previous frame by exceeding predetermined thresholds, the current video frame is assumed to be a scene change. Once a scene change is detected, the remainder of the video frame is encoded as an intra-frame, intra-macroblocks, or intra slices, through implementation of one or more predetermined or adaptively adjusted quantization parameters to reduce computational complexity, decrease power consumption, and increase the resulting video image quality. As decoding is the inverse of encoding, these improvements are similarly recognized by a decoder as it decodes a resulting encoded video stream.

The invention relates to the technical field of image information processing, in particular to a multiclass image classification method based on active learning and semi-supervised learning. The method comprises five steps: initial sample selection and classifier model training, BvSB active learning sample selection, CST semi-supervised learning, training sample set and classifier model updating and assorting process iteration. Through the operations of BvSB active learning sample selection, CST semi-supervised learning and SVM classification, the invention has efficient image classification effect under the condition of less manual tagging, does not increase overmuch computation burden, can quickly provide classification effects and also can take consideration of the demand of a classification system on the computation complexity.

A fast video encoder (100) and method (500) for selecting (809) Inter macro-block mode or intra macro-block mode is provided. The method can include estimating (502) a rate-distortion cost (801) of a plurality of predictors (203) for coding a video (201), selecting (504) a predictive coding mode for the video based on a minimization of the rate-distortion cost, and coding (505) the image using a predictive mode associated with the minimum rate-distortion cost. The rate-distortion cost can be estimated across Intra macro-block modes and Inter macro-block modes for reducing a computational complexity. Rate-Distortion costs can be terminated early (524) based on statistical information (522) across a plurality of predictors for increasing an encoding speed. A fast estimation of the rate-distortion cost associated with the Intra macro-block mode for making inter/intra macro-block mode decision in a video coding system by exploiting the coding statistics across prediction modes is presented.

The invention provides a visual ranging-based simultaneous localization and map construction method. The method includes the following steps that: a binocular image is acquired and corrected, so that a distortion-free binocular image can be obtained; feature extraction is performed on the distortion-free binocular image, so that feature point descriptors can be generated; feature point matching relations of the binocular image are established; the horizontal parallax of matching feature points is obtained according to the matching relations, and based on the parameters of a binocular image capture system, real space depth is calculated; the matching results of the feature points of a current frame and feature points in a world map are calculated; feature points which are wrongly matched with each other are removed, so that feature points which are successfully matched with each other can be obtained; a transform matrix of the coordinates of the feature points which are successfully matched with each other under a world coordinate system and the three-dimension coordinates of the feature points which are successfully matched with each other under a current reference coordinate system is calculated, and a pose change estimated value of the binocular image capture system relative to an initial position is obtained according to the transform matrix; and the world map is established and updated. The visual ranging-based simultaneous localization and map construction method of the invention has low computational complexity, centimeter-level positioning accuracy and unbiased characteristics of position estimation.

Methods for motion estimation with adaptive motion accuracy of the present invention include several techniques for computing motion vectors of high pixel accuracy with a minor increase in computation. One technique uses fast-search strategies in sub-pixel space that smartly searches for the best motion vectors. An alternate technique estimates high-accurate motion vectors using different interpolation filters at different stages in order to reduce computational complexity. Yet another technique uses rate-distortion criteria that adapts according to the different motion accuracies to determine both the best motion vectors and the best motion accuracies. Still another technique uses a VLC table that is interpreted differently at different coding units, according to the associated motion vector accuracy.

Finite-state systems and methods allow multiple input streams to be parsed and integrated by a single finite-state device. These systems and methods not only address multimodal recognition, but are also able to encode semantics and syntax into a single finite-state device. The finite-state device provides models for recognizing multimodal inputs, such as speech and gesture, and composes the meaning content from the various input streams into a single semantic representation. Compared to conventional multimodal recognition systems, finite-state systems and methods allow for compensation among the various input streams. Finite-state systems and methods allow one input stream to dynamically alter a recognition model used for another input stream, and can reduce the computational complexity of multidimensional multimodal parsing. Finite-state devices provide a well-understood probabilistic framework for combining the probability distributions associated with the various input streams and for selecting among competing multimodal interpretations.

A system for efficiently filtering interfering signals in a front end of a GPS receiver is disclosed. Such interfering signals can emanate from friendly, as well as unfriendly, sources. One embodiment includes a GPS receiver with a space-time adaptive processing (STAP) filter. At least a portion of the interfering signals are removed by applying weights to the inputs. One embodiment adaptively calculates and applies the weights by Fourier Transform convolution and Fourier Transform correlation. The Fourier Transform can be computed via a Fast Fourier Transform (FFT). This approach advantageously reduces computational complexity to practical levels. Another embodiment utilizes redundancy in the covariance matrix to further reduce computational complexity. In another embodiment, an improved FFT and an improved Inverse FFT further reduce computational complexity and improve speed. Advantageously, embodiments can efficiently null a relatively large number of jammers at a relatively low cost and with relatively low operating power.

A rapid vehicle license plate locating method is provided, which uses top hat transformation and character texture characteristic, and is divided into background subtraction, vehicle license plate region crude location and precision location three steps, specifically comprises: (1) background subtraction, converting obtained color vehicle license plate image into gray image, using top hat transformation to inhibit large-size background object, highlighting vehicle license plate region; (2) crude location phase, calculating image edge map and proceeding binaryzation, morphological dilation and connected components analysis and other operation, obtaining reasonable size vehicle license plate candidate region set; (3) precision location phase, extracting candidate region texture characteristic, using support vector machine classifier to classify candidate vehicle license plate region, thus accurately locating vehicle license plate region. The invention uses the top hat transformation to filter vehicle license plate image, reduces large-size background interference, so that greatly enhances environment adaptation; meanwhile, from crude to fine location method is more efficient, and reduces greatly computing complexity.

A mobile terminal is configured to receive wireless communications including GPS data from a terrestrial and/or satellite wireless network, and to perform pseudo-range measurements using the GPS data that is received. The mobile terminal may be configured to perform pseudo-range measurements by receiving GPS coarse/acquisition (C/A) signals from GPS satellites, estimating Doppler shifts in the received GPS C/A signals, and estimating received code phases of the GPS C/A signals using the Doppler shifts that are estimated. The estimated code phases and/or the estimated Doppler shifts of the GPS C/A signals can provide the pseudo-range measurements. By removing the Doppler shift from the received signal samples prior to performing the code phase measurement, reduced computational complexity and/or processing time may be obtained.

The embodiment of the invention discloses an image prediction method and a relevant device. The image prediction method comprises the following steps: determining the motion vector predictors of K pixel samples in a current image block, wherein K is an integer greater than 1, the K pixel samples comprise a first vertex angle pixel sample of the current image block, the motion vector predictor of the first vertex angle pixel sample is obtained on the basis of the motion vector of a preset first spatial domain adjacent image block of the current image block, and the first spatial domain adjacent image block is adjacent to a first vertex angle pixel sample spatial domain; and predicting the pixel value of the current image block on the basis of a non-translational motion model and the motion vector predictors of the K pixel samples. According to the scheme in the embodiment of the invention, the computation complexity of image prediction based on the non-translational motion model is lowered.

The invention is related to methods and apparatus that provide improved video encoding. Conventional video encoding techniques inadequately determine the encoding mode for a macroblock. One embodiment according to the invention advantageously includes the coding cost for motion vectors into the mode decision, thereby enhancing the quality of the selection of the mode. One embodiment further uses an L1 norm in the decision, thereby advantageously reducing the computational complexity of taking the coding cost for motion vectors into the mode decision.

An in-frame predication method for video encode in order to improve video encode quality is disclosed. The original video stream taken by camera is used as input, which is input to computer by video acquisition card, and then processed by computer and the JVT video encode technique. An operation rule for calculating the DC predication mode by use of the samples of the decoded pixel in adjacent blocks is defined. Multiple predication modes can be recombined and sorted.

Disclosed is a system, method, and apparatus for calculating metrics by using hierarchical level metadata to describe the various structures within the database. The hierarchical level metadata permit calculation of complex metrics by an analytical server which would otherwise be difficult or impossible. As a result of the way that the analytical server calculates the metrics, slicing and drilling are supported. Additionally, dimension and fact level security are also supported.

The invention belongs to the crossing field of computer vision and intelligent robots, relates to an indoor three-dimensional scene rebuilding method based on a double-layer rectification method and solves the problems that an existing indoor scene rebuilding method is expensive in required equipment, high in computation complexity and poor in real-time performance. The indoor three-dimensional scene rebuilding method based on the double-layer rectification method comprises Kinect calibration, SURF feature point extraction and matching, mapping from a feature point pair to a three-dimensional space point pair, three dimensional space point double-layer rectification based on random sample consensus (RANSAC) and inductively coupled plasma (ICP) methods and scene updating. According to the indoor three-dimensional scene rebuilding method based on the double-layer rectification method, the Kinect is adopted to obtain environmental data, and the double-layer rectification method is provided based on the RANSAC and the ICP. Indoor three-dimensional scene rebuilding which is economical and rapid is achieved, and the real-time performance of rebuilding algorithm and the rebuilding precision are effectively improved. The indoor three-dimensional scene rebuilding method based on the double-layer rectification method is applicable to the service robot field and other computer vision fields which are relative to the three-dimensional scene rebuilding.

The invention relates to a graph-based semi-supervised high-spectral remote sensing image classification method. The method comprises the following steps: extracting the features of an input image; randomly sampling M points from an unlabeled sample, constructing a set S with L marked points, constructing a set R with the rest of the points; calculating K adjacent points of the points in the sets S and R in the set S by use of a class probability distance; constructing two sparse matrixes WSS and WSR by a linear representation method; using label propagation to obtain a label function F<*><S>, and calculating the label prediction function F<*><R> of the sample points in the set R to determine the labels of all the pixel points of the input image. According to the method, the adjacent points of the sample points can be calculated by use of the class probability distance, and the accurate classification of high-spectral images can be achieved by utilizing semi-supervised conduction, thus the calculation complexity is greatly reduced; in addition, the problem that the graph-based semi-supervised learning algorithm can not be used for large-scale data processing is solved, and the calculation efficiency can be improved by at least 20-50 times within the per unit time when the method provided by the invention is used, and the visual effects of the classified result graphs are good.

The invention discloses an image forecasting method and a related device. The image forecasting method includes: confirming K1 pixel samples in an image block x, and confirming a candidate movement information unit set corresponding to each of the K1 pixel samples; enabling each candidate movement information unit set corresponding to each pixel sample to include at least one candidate movement information unit; confirming a merger movement information unit set i which includes K1 movement information units; using a non-translation movement model and the merger movement information unit set i to forecast pixel values of the image block x, wherein all the movement information units in the merger movement information unit set i are respectively selected from at least a part of the movement information units in the candidate movement information unit sets corresponding to the different pixel samples in the K1 pixel samples. The image forecasting method and the related device facilitate reduction of computation complexity of image forecasting based on the non-translation movement model.